In recent years, complementary recommendation has received extensive
attention in the e-commerce domain. In this paper, we comprehensively summarize
and compare 34 representative studies conducted between 2009 and 2024. Firstly,
we compare the data and methods used for modeling complementary relationships
between products, including simple complementarity and more complex scenarios
such as asymmetric complementarity, the coexistence of substitution and
complementarity relationships between products, and varying degrees of
complementarity between different pairs of products. Next, we classify and
compare the models based on the research problems of complementary
recommendation, such as diversity, personalization, and cold-start.
Furthermore, we provide a comparative analysis of experimental results from
different studies conducted on the same dataset, which helps identify the
strengths and weaknesses of the research. Compared to previous surveys, this
paper provides a more updated and comprehensive summary of the research,
discusses future research directions, and contributes to the advancement of
this field.

本研究对近年来电子商务领域中互补推荐进行了广泛的综述和比较，包括模型互补关系的数据和方法、互补推荐的研究问题以及实验结果的比较分析，并提供了对该领域未来研究方向的讨论，从而为该领域的进展做出了贡献。

电子商务中的互补推荐：定义、方法和未来方向

Complementary Recommendation in E-commerce: Definition, Approaches, and  Future Directions

Existing Neural Machine Translation (NMT) models mainly handle translation in
the general domain, while overlooking domains with special writing formulas,
such as e-commerce and legal documents. Taking e-commerce as an example, the
texts usually include amounts of domain-related words and have more grammar
problems, which leads to inferior performances of current NMT methods. To
address these problems, we collect two domain-related resources, including a
set of term pairs (aligned Chinese-English bilingual terms) and a parallel
corpus annotated for the e-commerce domain. Furthermore, we propose a two-step
fine-tuning paradigm (named G2ST) with self-contrastive semantic enhancement to
transfer one general NMT model to the specialized NMT model for e-commerce. The
paradigm can be used for the NMT models based on Large language models (LLMs).
Extensive evaluations on real e-commerce titles demonstrate the superior
translation quality and robustness of our G2ST approach, as compared with
state-of-the-art NMT models such as LLaMA, Qwen, GPT-3.5, and even GPT-4.

利用两个领域相关资源，提出了一个两步细调范式来将一般 NMT 模型转化为专门用于电商领域的 NMT 模型，该范式基于大型语言模型（LLMs），通过自对比语义增强来提高翻译质量和鲁棒性。与最先进的 NMT 模型（如 LLaMA、Qwen、GPT-3.5 甚至 GPT-4）相比，对真实电商标题进行的广泛评估表明，我们的 G2ST 方法具有卓越的翻译质量和鲁棒性。

电子商务领域的通用到专业的 LLMs 翻译

General2Specialized LLMs Translation for E-commerce

In this paper, we propose a new setting for generating product descriptions
from images, augmented by marketing keywords. It leverages the combined power
of visual and textual information to create descriptions that are more tailored
to the unique features of products. For this setting, previous methods utilize
visual and textual encoders to encode the image and keywords and employ a
language model-based decoder to generate the product description. However, the
generated description is often inaccurate and generic since same-category
products have similar copy-writings, and optimizing the overall framework on
large-scale samples makes models concentrate on common words yet ignore the
product features. To alleviate the issue, we present a simple and effective
Multimodal In-Context Tuning approach, named ModICT, which introduces a similar
product sample as the reference and utilizes the in-context learning capability
of language models to produce the description. During training, we keep the
visual encoder and language model frozen, focusing on optimizing the modules
responsible for creating multimodal in-context references and dynamic prompts.
This approach preserves the language generation prowess of large language
models (LLMs), facilitating a substantial increase in description diversity. To
assess the effectiveness of ModICT across various language model scales and
types, we collect data from three distinct product categories within the
E-commerce domain. Extensive experiments demonstrate that ModICT significantly
improves the accuracy (by up to 3.3% on Rouge-L) and diversity (by up to 9.4%
on D-5) of generated results compared to conventional methods. Our findings
underscore the potential of ModICT as a valuable tool for enhancing automatic
generation of product descriptions in a wide range of applications.

本文提出了一种新的设置，通过加入营销关键词生成与产品特点更符合的图像描述。为解决同类产品拷贝文字相似导致生成描述不准确的问题，引入了名为 ModICT 的多模态文本调整方法，通过类似产品样本作为参考，利用语言模型的上下文学习能力生成描述。实验证明，与传统方法相比，ModICT 显著提高了生成结果的准确性（Rouge-L 提升了 3.3%）和多样性（D-5 提升了 9.4%），可用于增强各种应用中自动生成产品描述的效果。